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International Journal of Economics and Financial Issues
Vol. 3, No. 4, 2013, pp.827-843
ISSN: 2146-4138
ww.econjournals.com
The Dynamic Relationship of Stock Indexes on Interbank Money Market
Rates: Evidence from Thailand
Zongjun Wang
Room552, School of Management,
Huazhong University of Science and Technology,
1037 Luoyu Road, Wuhan City, Hubei Province,
CHINA, 430074. Email: [email protected]
Gongkhonkwa Rujira (Corresponding Author)
Building7 Room104, Huahong Foreign Student Apartment, C3 District,
Huazhong University of Science and Technology, Hongshan Area, Wuhan City,
Hubei Province, CHINA 430074. Email: [email protected]
ABSTRACT: Many researches shed light on investigation the several variables that might be able to
influence economic system, this research play an important to identify the relationship of stock
indexes on interbank money market rates, we make used of a secondary data by based upon the top
ten largest stock exchanges in the world and BIBOR from 2006 to 2011 and applied the simple linear
regression model as our model. Over the whole sample period, the result that we have found from the
variance decomposition analysis and impulse response analysis there are three important stock
indexes which lead up to the BIBOR changes that consists of DJIA, FTSE100, and ASX. Another
interesting feature found in this study is that from the Granger causality analysis, the DJIA, NASDAQ,
NIKKEI225, FTSE100, TSX, SSE, BOVESPA, ASX, and DAX were found to directly causality on
the BIBOR, except the HSI over the sample period.
Keywords: Interbank money market; Bangkok Interbank Offered Rate; Stock indexes
JEL Classifications: G01; G21; G32
1. Introduction
Given the globalization of financial market or several new markets in recent years, it can move
money or financial product freely between markets. Thus, the effect of a growth very rapidly in
financial market, it is able to increase the volatility of market. As a result, every sector of economy
have pay more attention and focus on the recent globalization era to set up some mechanism or
seeking the suitable way to solve that problem, to make the economic and financial sectors stability,
and soundness of economic system. As, the past few years, financial crisis continued to occur from
2007 till present which cause the recession and also can be lead to a domino effect of economic
systems that be transfer between countries. Hence, several part of economic sector have been analyzed
the impact of financial turmoil in economic system, for instance, the relative response of financial
market, stock market, business cycle to financial turmoil when the global markets have changed.
An important point for interest rate risk is it can affect the other economic variables and changes in
the interest rate will lead to an increase in the cost of money which relative to the expected inflation
rate, the profitability of investment opportunities and so on. Indeed, the interest rate is a one tool for
moving the economic system in the era of the global financial integration, in which interfere with the
efficiency of financial intermediaries and market to move funds to people and companies. Therefore,
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International Journal of Economics and Financial Issues, Vol. 3, No. 4, 2013, pp.827-843
to ensure the soundness and stability of the financial system the interest rate should be reflect to all
factors and conditions that perhaps have a significant sensitivity when the market situation has
changes which in accordance with the efficient market hypothesis that the prices in market should be
fully reflected all economic variables and the uncertainties of macroeconomic fundamentals (Azad et
al, 2012).
The interest rate is a one significant part of Thailand’s economy owing to the Bank of Thailand
(BOT) used the interest rate to control the inflation rate and economic growth rate in Thailand. Indeed,
the BOT’s actions affect interest rate, the money supply, and all of which have direct and indirect
impacts on financial market. As, Giorgio and Zeno (2011) mentioned that “the central bank is care
about monetary policy and financial stability that it might able to be suggestion to assign to central
banks a preferred target in term of price stability”. The one of the committees which working with the
BOT is the Monetary Policy Committee (MPC), they are work and play an important point to the
government’s policies, the country’s economics, and financial environment. Generally, the MPC
meets approximately 6 weeks to makes policy decision and to evaluate the determinant of economic
and monetary condition by based upon the risk variables, such as the world oil prices, monetary policy
in other countries and the world commodity prices which affect an inflation and economic growth.
Both of internal and external economic conditions are affect the country’s policy and it able to be an
indicator for forecast or predict what will happen to the factors that affect the economic system in the
next period. Accordingly, the economist should make assessments and the policy decision by base on
all conditions that able to be an affect to economic system as well.
An important feature of our analysis here is to understand the stock indexes that determine the
changes of interbank money market rates, this research we capture particularly on the interrelationship
between two parts of an economic sector that consist of the interbank money market and the stock
market, we will analyze the interbank money market rate and the stock index then conclude the
dynamic relationship on it. We would like to take an action to more understanding and improve our
knowledge on the sensitivity of interbank money market to changes in the stock market. Similarly, we
will specify which stock index has more significant affect and be able to forecast the interbank money
market rate in Thailand.
The knowledge of the response of BIBOR to stock indexes is crucial not only for the financial
institution or financial companies, but also for everyone who takes an interest in interbank money
market rate and stock indexes for purposes risk management, portfolio management, asset pricing, and
so on. The remainder of the paper is structured as follows. Section 2 provides the theoretical
background and literature review. Section 3 details the data and methodology in this study.
Descriptions of econometric results are present in section 4. Finally, section 5 conclusions.
2. Theoretical Background and Literature Review
2.1 Definition of Interbank money market
A financial market is a market in which to channel funds from savers (lenders) who have an excess
of funds to spenders (borrows) who have a shortage of funds (Mishkin, 2004), a market that provide
financing for business. A well-functional of financial market is a key factor in economic growth which
has direct and indirect effects on personal wealth, the business behavior, the consumer behavior, and
the cyclical of the economy as well. In recent years, financial market is complex and comprising many
different types of market, for instance, bond market, stock market, foreign exchange market, money
market. Therefore, all of financial markets should relevant to each other in which when one market
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The Dynamic Relationship of Stock Indexes on Interbank Money Market Rates: Evidence from
Thailand
face fluctuates the other market should be face that affect too which in accordance with the efficient
market hypothesis (EMH). As the theory of the efficient market (Fama, 1970) that mentioned
“anytime the information can be effect to the prices and fully reflect available information in the
market”. As, Mishkin (2004) noted more detail definition that is “all prices are always correct and
reflect market fundamental”. In addition to, the efficient market hypothesis, it can classify into three
types which consist of, 1) weak-form, 2) semi strong-form, and 3) strong-form (Shleifer, 2000;
Stephen et al., 2005). Consequently, interbank rates adjustment must reflect instantaneously to news,
information, and economic variables in the market following the efficient market hypothesis.
Therefore, researchers must take into account the efficient market hypothesis in their analysis of
financial market by investigating all conditions that might affect to the prices in market and market
fluctuation to increase high understanding for reducing their risk and to plan for the future.
The interbank money market refers to a market which transfers funds from one to the other or a
moving of funds from lenders to borrowers by financial market, the markets for trading in the money
market such a short-term debt or securities which the debt or securities are issues with maturities of at
less than 1 year. Nevertheless, the commercial bank lending had an impact from the monetary policy
and relation on the economic activities (June and Pronkamol, 2007). As, Anoop et al. (2007) noted
that “when monetary policy had changes many countries also change their discount rates especially
the short-term rates than long-term rates”. In practice, the most lenders and borrowers in interbank
money market are the financial institutions such a bank. For transactions, the bank borrowers must
pay for borrowing the money to the bank lender with the interbank rate.
A financial institution is important sources of financing that provide a financial service such raise
money from investors and provides financing for other investors who want to borrow the money.
Simultaneously, the financial institution’ aim is to lend to good borrowers and reject bad borrowers
(Xiaofen, 2007) because they are concerning about the default risk however Tor and Kasper (2003)
have found the size of small consumer loan does not influence default risk of a bank lending policy.
Besides, Leonardo et al (2004) suggested that “the response of bank lending to a monetary policy
shock has an expected negative sign”. For corporations, financial institution is an intermediary that
service to both of the companies and depositors. Usually, to cover the bank’s cost of service the
financial institution will charge the borrowers with a higher interest rate than they pay for depositors.
And basically, the financial institutions become more concern about reducing the risk that their
institutions faced and they also want to be sure that the counterparty be honest and not default on the
loan or obligations.
Indeed, there are many variables that might cause of fluctuations both of direct and indirect in
interest rate, such as common macroeconomic risk factors (Mathias et al, 2010). And, we also can call
those variables that able to fluctuate in interest rate as risk in which the degree of risk or uncertainty
can affect the demand and supply of funds as well. Theoretically, there are two types of risk;
systematic risk and unsystematic risk (Stephen et al, 2005). In systematic risk, it is the part of an
asset’s risk which refers to any risks that affect a large number of assets, each to a greater or lesser
degree. In unsystematic risk, refers to a risk that specifically affects a single asset or a small group of
asset thereby it able to be eliminated in a diversified portfolio (Mishkin, 2004). According to the
definition of risk or uncertainty above, we can write the total risk of an asset as being made up of
systematic risk and unsystematic risk:
Total Asset Risk = Systematic Risk + Unsystematic Risk
(1)
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2.2 Definition of Interbank offered rates
The financial institution and financial companies face the important effect from the interest risk
and default risk (Sanjay, 1996; Mathias et al, 2010) especially during the financial crisis which fiscal
policies in many countries had led to increased fears of sovereign default (Andreas, 2011). As a cause
of the sovereign debt of Spain, Portugal, Ireland, Greece and other developing countries that had been
downgraded in credit rating (Huixin, 2012). However, the improving of country’s international credit
has an effect from the monetary policy (Paschakis and Smithin, 1998) and decrease in default risk
able to reduce the interest rate spread (Noh-Sun, 2002). In addition, high volatility in interest rate with
the significant level of financial leverage has contributed to the growing relevance of interest rate
exposure (Laura et al, 2011).
An interest rate is a cost of borrowing that borrows must pay for lending the money from lender in
which the interest rate usually expressed as a percentage of the principle. Besides, changes in the
interest rates have a significant impact on financial institutions, business, and the overall economy as
well.
In the past decade till recently, many financial economists engage in research on the effect of
financial turmoil that can have a devastation effect on economic system, monetary policy, financial
fluctuation, stock volatility and among other topics. Likewise, they have increase their focus and
explain the volatility in interest rates because of the interest rates are a significant part of economy
and have an effect on the overall health of the economy such a willingness to spend or save and
investment decisions. In actually, there are many interest rates in the economic system in which we
can separate the interest rates into two types, 1) short-term interest rate is the interest rate on a loan
with a maturity not excess one year, and 2) long-term interest rate is the interest rate on loan that
maturity over one year. Generally, the short-term interest rates are also called money market rates in
which the interbank money market rate is the one of short-term interest rate. In monetary policy, the
short-term interest rate is more significant effect on it. Following some evidence from previous study,
short-term interest rate in financial market such a short term securities repurchase agreements (repos)
does a one of indicator in monetary policy (Kay and Dieter, 2001), and also an important input for
business cycle analysis (Chew et al, 2013). However, the volatility in financial markets (Faff and
Haward, 1999), and the macroeconomic variables have an influence on short-term interest rate
(Thomas and Michele, 2007) which consistent result with Ming et al. (2008), they noted that “the
monetary policy rate had influence and faster adjustment speed on short-term interest rate”. Similarly,
monetary policy also had a significant impact on long-term interest rate (Hakan and Richard, 2009).
Thailand’s interbank money market rate is called the Bangkok Interbank Offered Rate (BIBOR),
which quoted by the Bank of Thailand (BOT) in every working day at 11:15 AM (Bangkok time). The
BIBOR is the interest rate in which banks can borrow THB funds from other banks in the Bangkok
interbank market. Formally, in Thailand’s money market, the BIBOR was effective from 2005 and the
BOT try to promote the BIBOR to financial institutions for using the BIBOR to be a reference rate or
underlying in financial transactions. The tenors of BIBOR are composed of, 1 week, 1 month, 2
months, 3 months, 6 months, 9 months, and 12 months. In economics, short-term interest rate such
interbank money market rate is the better indicator to specify the monetary policy and it can reflect
the real situation in financial market.
In order to calculate the interest rate following the economic theory we have to consider the vital
variables that might affect the supply and demand of funds. Basically, in the case of a simple loan the
formula for calculation a simple interest rate is follows equation:
I =Px rxt
(2)
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The Dynamic Relationship of Stock Indexes on Interbank Money Market Rates: Evidence from
Thailand
where I is the interest rate, P is the principle, r is the percentage of interest rate, and t is the timing of
loan.
Theoretically, change in variables that might influence the interest rate changes such income, and
money supply that able to shift the interest rate as follows:
Figure 1. Interest rate shift when money demand and money supply changes (adopt from Mishkin, 2004)
Notes:
= Interest Rate,
= Quantity of Money,
= Money Demand,
= Money Supply
As we can see in figure 1, the changes of money demand and money supply on interest rate. In
economic system, the amount of money demand and money supply both have a significant effect to
the interest rate. When the money demand increase owing to during a business cycle expansion or
when recovery begin in which other economic variables are constant as a result the interest rate will
increase. Conversely, in a recession the money supply rises when everything else remaining equal
thereby an interest rate will decrease.
In practice, all of financial transactions in Thailand’s money market are only use a reference rate
such BIBOR, THBFIX to be an underlying asset. And, the method to calculate the BIBOR is the BOT
will calculated from the average of rate which derived by eliminating the first two highest and lowest
of the quotes and arithmetic-averaging the remaining rates for the day. Meanwhile, the method to
calculate the THBFIX or Thai Baht Implied Interest Rate, which refers to an interest rate for lending
in Thai Baht, it is defined by the following formula:
(3)
where THBFIX is the Thai Baht Interest Rate Fixing, FWD is the average USD/THB forward
exchange rate, SPOT is the average USD/THB spot exchange rate, SIBOR is the US Interest rate for
the respective tenor, DAYS is the number of day.
Usually, the data for the BIBOR is published by the BOT at 11:15 AM (Bangkok time) every
working day, the THBFIX is published by the Reuters at 11:00 AM (Bangkok time) every business
day.
As mentioned before, the issue of interest rate risk is more interest to everyone and there are many
variables that might impact on interest rate. As, Elyas and Iqbal (1998) noted that “a sensitively of the
bank stock returns distribution has a significant impact from the interest rate and interest rate
volatility”, Oldrich (2005) described the interest rate should be set by supply and demand of
borrowing and lending, production opportunities in the economy, time preference for consumption,
attitude toward risk, return of the participants in the economy, and the distribution of wealth across the
participants”. Accordingly, in this study we have assume the sensitivity of an interest rate such
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International Journal of Economics and Financial Issues, Vol. 3, No. 4, 2013, pp.827-843
BIBOR might has a significant impact from the economic variable such the stock indexes thereby we
will explore the correlation between Thailand’s money market rate such BIBOR and stock indexes to
understand the response of the BIBOR to the changes of stock indexes during the sample period.
2.3 Definition of stock market
The stock markets are also called equity market which refers to a market where securities such a
share of stock are issued and traded. Moreover, the stock market is probably the one important
financial market in which when the company decides to go public for helping the firm’s investment
the one choice is by issuing shares on a stock market (primary market) that called an initial public
offering (IPO) and the buyers become part-owners of the firm. However, if the investors do not want
to hold their securities anymore the secondary market allows investors to trade securities among
themselves.
Not surprisingly, the financial system is complex in structure and function as a result the financial
market becomes a dramatic increase in risk or uncertainty. Obviously, many stock markets are moving
toward trading the stock, simultaneously the investors are trying to explore the new investment
channel to add more their investment opportunity as well as to decrease risk that may effect to their
profitability. As a result, in the past few years until recently, we will find not only an investor who
wants to avoid risk but also the financial institutions, they are decided to eliminate the risk that could
happen in the future.
In the past, stock prices have been extremely volatile particularly during the financial crisis that
started from the end of 2007, this is a major disruption in financial market because of the crisis is
caused by instability of the market and lead to a contraction in economic activity. Similarly,
fluctuation of the stock prices in stock market is very excited and affects the size of investor’s wealth
and their willingness to spend. As, Thomas et al (2006) suggested that “the stock market situation
such a bluish market can motivate and be very attractive for investor behavior.
As well-know, many previous studies claimed that the stock market risk is about the volatility of
the stock prices with many variables in economic system. However, some evidence found the variable
in which might affect the prices stability is the monetary policy (Viv and Devid, 1998), moreover and
an affect of global, country and industry factors had a significant sensitivity when the stock market
fluctuation (Mohan and Shawkat, 2007). Likewise, Hui (2004) noted that “the federal funds rate target
and the monetary shocks had a significant impact to stock prices especially when the business
conditions is bad”, the politics, economy, and financial sector both of inside and outside of countries
have an effect to stock prices, the investor’s markets sentiment and the instability of market (Kasimir
and Lasse, 2008), and consistent with some evidence of Chun-Li (2011) and Timo (2011) they have
found the stock returns are reached to unanticipated increase in the federal funds rate.
Besides, for this study we take into account to analysis the correlation between interbank money
market rates and stock indexes in case of Thailand’s money market in which some evidence of
previous study that related to our study is Praphan and Subhash (2002), they have suggested that an
interest rate had a negative long run relationship on stock prices in Philippines, Singapore and
Thailand whereas a positive relation in Indonesia and Malaysia. And, the money growth in Malaysia,
Singapore, and Thailand has a positive impact to stock market.
Consequently, there are several reasons to take an interest in stock market risk and interest rate risk,
Noh-Sun (2002) mentioned that “the financial variables such stock prices and interest rate are adjusted
to new information much faster than real variables therefore it have been used as lending indicators to
predict future condition of the real economy”, Federico (2002) suggested that “it as a key economic
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The Dynamic Relationship of Stock Indexes on Interbank Money Market Rates: Evidence from
Thailand
variable linking and monetary phenomena which it had implications for the pricing of fixed-income
securities and derivatives, and it was a benchmark to evaluate asset pricing”. Therefore, in this study
we would like to explain the movements in the stock indexes affect BIBOR and the overall correlation
between variables by making a clearly understand and help us answer questions how changes in the
stock indexes will affect the Thailand’s money market rate such BIBOR.
3. Data and Methodology
3.1 Data
To examine the sensitivity of the interbank money market rates to changes in the stock indexes, in
this study we have collected the secondary data from the Bank of Thailand (BOT) that cover the
sample period from January 2006 to December 2011. The time series data of interbank money market
rate is the BIBOR for tenor running from 1 week, 1 month, 2 months, 3 months, 6 months, 9 months,
and 12 months. Consider ten stock indexes to be our independent variable, we base the data on the ten
largest
stock
exchanges
in
the
world
by
market
capitalization
in
2011
(www.world-stock-exchanges.net/top10.html), are composed of Dow Jones Industrial Average (DJIA),
NASDAQ Composite (NASDAQ), Nikkei heikin kabuka (NIKKEI225), FTSE (FTSE100), SSE
Composite (SSE), Hang Seng (HSI), TSX Composite (TSX), BM&F Bovespa (BOVESPA),
S&P/ASX (ASX), and Deutscher Aktien (DAX).
For reason why we examine the relationship between BIBOR and stock indexes is that because of
the interest rate is a one part of monetary policy in which the central bank such BOT used it for control
the economic growth. Moreover, as well-know, the top ten of stock markets in the world have a
significant impact on the other stock market which has a market capitalization smaller than them.
Besides, we are in the global market era that several risks in one market able to be affected other
market which in accordance with the efficient market hypothesis. Thus, in this study we have assumed
the interbank money market should response to the stock index because they are in the financial
markets. For this study we based the stock indexes on the top ten largest stock markets in the world by
market capitalization and we intently to assess which stock index has important significant effect on
BIBOR.
3.2 The econometric model
In order to explore the correlation between variables, there are many tools to study and explain on
it however in this study we have focus only on the relationship between BIBOR and stock indexes
thereby we have applied a simple model of regression model in econometric test to be our model to
help us understand a movement in stock index that have occur the changes of BIBOR by using the
BIBOR to be a dependent variable and the stock indexes to be an independent variable. Following
Engle and Granger, we have applied the multiple linear regression models as follows:
(4)
where α0 and α1 are a parameters, BIBORt is the Bangkok Interbank Offered Rate, Stock Indexest is
the top ten largest stock market in the world by market capitalization which consist of DJIA,
NASDAQ, NIKKEI225, FTSE100, SSE, HSI, TSX, BOVESPA, ASX, and DAX, and t is an Error
term.
This equation tries to telling us about the correlation between dependent variable and independent
variable in which our hypothesis in this study we have assumed the BIBOR and stock index should be
relevant to each other. Consequently, as mentioned above we will start to explore the relationship
between variables with the econometric test start from unit root test, Granger causality analysis,
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International Journal of Economics and Financial Issues, Vol. 3, No. 4, 2013, pp.827-843
variance decomposition analysis, and impulse response function analysis, respectively. The result from
this study will describe the stock indexes process on BIBOR changes to help us understand the
complexity of Thailand’s money market rates.
4. Econometric Results
As mentioned in previous part, this study we have used the econometric test to explain the
interrelationship between our variables by modify the multiple regression model. We specific
investigated the dynamic relationship of the top ten stock indexes in year 2011 by based on the market
capitalization on BIBOR during the sample period running from January 2006 to December 2011.
Then, we have presented the econometric result as follows, the first subsection was the unit root test,
the second subsection was the Granger causality analysis, the third subsection was the variance
decomposition analysis, and the last subsection was the impulse response function analysis.
4.1Unit Root Test
Before going on to analyze the correlation between the BIBOR and stock indexes, firstly we must
taking into account the stationary of data. Regarding our study using the time series data of BIBOR
and stock index in which the data might non-stationary thus it be able to effect to the result of
econometric test as a spurious regression or a spurious relationship between variables thereby we must
test a stationary before investigate the interrelationship by econometric test. For this study, our tool to
test a stationary we will use a unit root test with Augmented Dickey-Fuller (ADF).
Table 1. Unit root-ADF test (Trend and intercept)
Variable
Level
Lag1
Equation test
t-statistic
MacKinnon
Critical
BIBOR 1 week
-3.40968
-3.16505***
BIBOR 1 month
-3.41096
-3.16505***
BIBOR 2 months
-3.50547
-3.47531**
BIBOR 3 months
-3.49480
-3.47531**
BIBOR 6 months
-3.43216
-3.16505***
BIBOR 9 months
-3.44452
-3.16505***
BIBOR 12 months
-3.49548
-3.47531**
-6.61169
-4.09455*
-7.03415
-4.09455*
-7.30297
-4.09455*
FTSE100
-7.66589
-4.09455*
SSE
-8.42820
-4.09455*
HSI
-7.39586
-4.09455*
TSX
-6.53811
-4.09455*
BOVESPA
-6.88139
-4.09455*
ASX
-6.28011
-4.09455*
-7.09778
-4.09455*
DJIA
NASDAQ
NIKKEI225
DAX
st
1 difference
0
With trend
and intercept
1
Note: Lag = Length (Automatic based on SIC); * = Significant at 0.01; ** = Significant at 0.05; *** = Significant at 0.10.
Following the role of stationary in which the t-statistic value must less than the MacKinnon critical
value. From the unit root test with ADF in table 1 shows us, all of our variables had the t-statistic
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The Dynamic Relationship of Stock Indexes on Interbank Money Market Rates: Evidence from
Thailand
value less than the Mackinnon critical value. The BIBOR 1 week, 1 month, 2 months, 3 months, 6
months, 9 months, and 12 months were rejected the null hypothesis at the 1st difference. Also, the
stock index which consists of the DJIA, NASDAQ, NIKKEI 225, FTSE 100, SSE, HIS, TSX,
BOVESPA, ASX, and DAX were rejected the null hypothesis at the 1st difference. To sum up, our
variables were stationary at 1st difference with the significant level at 1%, 5%, and 10%.
4.2 Granger Causality analysis
Obviously, the performance of an economic in every countries are depending on the monetary
policy, such as money supply, exchange rate, and interest rate, and the fiscal policy such government’s
revenue and expenses in which both of monetary and fiscal policies must influence each other.
Therefore, that suggest the interbank money market rates and stock indexes should have a significant
effect to the performance of an economic as well. Thus, this subsection intends to analyze the
causality of the variables to explain the causes and effects following Granger (1969). We have used
the above concept to explain the relationship between interbank money market rates and stock
indexes.
Table 2. Granger Causality Test
p - values
Null Hypothesis:
1
2
3
6
9
12
1 week
month
months
months
months
months
months
DJIA does not Granger Cause BIBOR
0.1060*
0.1443*
0.1303*
0.1307*
0.1415*
0.1383*
0.1354*
BIBOR does not Granger Cause DJIA
0.7260
0.8606
0.8476
0.8051
0.7639
0.7571
0.7581
NASDAQ does not Granger Cause BIBOR
0.0432*
0.0563*
0.0486*
0.0498*
0.0607*
0.0650*
0.0723*
BIBOR does not Granger Cause NASDAQ
0.7483
0.7355
0.7117
0.6999
0.6639
0.6228
0.5862
NIKKEI 225 does not Granger Cause BIBOR
0.0593*
0.0540*
0.0646*
0.0749*
0.0823*
0.0823*
0.0819*
BIBOR does not Granger Cause NIKKEI225
0.7584
0.6020
0.6289
0.6904
0.8013
0.8520
0.8972
FTSE 100 does not Granger Cause BIBOR
0.0375*
0.0295*
0.0298*
0.0296*
0.0314*
0.0327*
0.0334*
BIBOR does not Granger Cause FTSE100
0.8672
0.9847
0.9913
0.9854
0.9688
0.9567
0.9410
SSE does not Granger Cause BIBOR
0.6385
0.1213*
0.1313*
0.1865
0.3532
0.4309
0.5341
BIBOR does not Granger Cause SSE
0.0964*
0.0763*
0.0879*
0.1083*
0.1825
0.2580
0.3265
HSI does not Granger Cause BIBOR
0.2373
0.2383
0.2326
0.2320
0.2516
0.2545
0.2633
BIBOR does not Granger Cause HSI
0.5601
0.4149
0.4228
0.4776
0.5507
0.5489
0.5237
TSX does not Granger Cause BIBOR
0.0291*
0.0267*
0.0243*
0.0237*
0.0273*
0.0260*
0.0276*
BIBOR does not Granger Cause TSX
0.8934
0.8038
0.7915
0.8226
0.8602
0.8675
0.8440
BOVESPA does not Granger Cause BIBOR
0.2601
0.2681
0.2334
0.1969
0.1469*
0.1177*
0.0918*
BIBOR does not Granger Cause BOVESPA
0.0350*
0.0202*
0.0189*
0.0196*
0.0218*
0.0191*
0.0157*
ASX does not Granger Cause BIBOR
0.2177
0.2194
0.1879
0.1572
0.1480*
0.1078*
0.0930*
BIBOR does not Granger Cause ASX
0.7629
0.5459
0.5071
0.5615
0.6473
0.7027
0.7307
DAX does not Granger Cause BIBOR
0.1270*
0.1767
0.1757
0.1581
0.1363*
0.1276*
0.1100*
BIBOR does not Granger Cause DAX
0.9449
0.9475
0.9149
0.9275
0.9349
0.9324
0.9289
Note: * = reject null hypothesis at significant level 10%
To clearly understand in the causality which stock index does a cause the BIBOR and how much
causality on it, from table 2 we have found the results of causality test between BIBOR and stock
index are as follows:
835
International Journal of Economics and Financial Issues, Vol. 3, No. 4, 2013, pp.827-843
1.) The causality of the BIBOR and DJIA: the DJIA does a cause BIBOR in every tenor.
2.) The causality of the BIBOR and NASDAQ: the NASDAQ does a cause BIBOR in every tenor.
3.) The causality of the BIBOR and NIKKEI225: the NIKKEI225 does a cause BIBOR in every tenor.
4.) The causality of the BIBOR and FTSE100: the FTSE100 does a cause BIBOR in every tenor.
5.) The causality of the BIBOR and SSE: The SSE does a cause BIBOR only tenor 1 month and 2
months.
6.) The causality of the BIBOR and HSI: the HIS does not a cause BIBOR in every tenor.
7.) The causality of the BIBOR and TSX: the TSX does a cause BIBOR in every tenor.
8.) The causality of the BIBOR and BOVESPA: the BOVESPA does a cause BIBOR only in tenor 6
months, 9 months, and 12 months.
9.) The causality of the BIBOR and ASX: the ASX does a cause BIBOR only in tenor 6 months, 9
months, and 12 months.
10.) The causality of the BIBOR and DAX: the DAX does a cause BIBOR in tenor 1 week, 6 months,
9 months, and 12 months.
To summarize, the causality of stock index on BIBOR follow the Granger causality analysis that
suggest the DJIA, NASDAQ, NIKKEI225, FTSE100, and TSX were causality on BIBOR in every
tenor. Besides, the SSE, BOVESPA, ASX, and DAX were causality on BIBOR in some tenor.
Conversely, the HSI was the only one stock index does not cause BIBOR in every tenor.
4.3 Variance Decomposition analysis
According to our assumption, the uncertainty about the stock market might have a significant
impact to the interbank money market therefore the financial institutions which trading on interbank
money market should pay more attention on the stock market because of the information or news in
both internal and external of market always implied to each other as well. Hence, all activities that
happen on the market will be a positive or negative impact to financial institutions so we called the
bank’s risk.
In previous subsection, we have investigated the causality of stock indexes on BIBOR to answer
our assumption which stock index does a cause the BIBOR. Here, our analysis in this subsection we
need to examine the sensitivity of the BIBOR to changes in the stock indexes by using the variance
decomposition analysis for explore the short-run and long-run relationships of our variables.
As we have seen from variance decomposition analysis in table 3, the DJIA, FTSE100, and ASX
seem like they had more significant relation on BIBOR in every tenor than other stock indexes.
Additionally, we also found the stock indexes were varying directly with the tenor of BIBOR.
Table 3. Variance Decomposition Test
NIKKEI
FTSE
Period
S.E.
BIBOR
1w
DJIA
NASDAQ
225
100
SSE
HSI
TSX
BOVESPA
ASX
DAX
1
0.121
100.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
2
0.198
97.198
0.102
0.263
0.026
0.001
0.329
0.444
0.004
1.196
0.437
0.000
3
0.276
87.451
0.843
0.173
1.327
0.126
0.220
2.918
0.243
3.249
3.450
0.000
4
0.371
74.341
3.034
0.158
3.670
0.915
0.123
3.793
1.713
5.064
7.166
0.023
5
0.479
63.061
5.858
0.320
5.373
2.608
0.137
3.120
3.149
5.785
10.549
0.040
6
0.591
54.906
8.529
0.488
6.336
4.749
0.269
2.276
3.833
5.914
12.674
0.026
7
0.695
49.328
10.809
0.598
6.723
7.006
0.455
1.672
3.747
5.815
13.815
0.033
8
0.787
45.558
12.609
0.659
6.740
9.248
0.655
1.305
3.275
5.687
14.191
0.073
836
The Dynamic Relationship of Stock Indexes on Interbank Money Market Rates: Evidence from
Thailand
9
0.864
42.932
13.965
0.695
6.517
11.352
0.855
1.096
2.762
5.601
14.064
0.161
10
0.928
40.959
14.960
0.720
6.198
13.191
1.048
0.980
2.403
5.563
13.670
0.307
NIKKEI
FTSE
BIBOR
Period
S.E.
1m
DJIA
NASDAQ
225
100
SSE
HSI
TSX
BOVESPA
ASX
DAX
1
0.111
100.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
2
0.191
94.920
0.643
0.659
0.000
0.403
1.529
1.767
0.013
0.026
0.026
0.014
3
0.267
85.993
0.773
0.400
2.260
1.622
1.086
5.101
0.585
0.227
1.905
0.048
4
0.363
73.436
2.818
0.217
5.705
2.817
0.637
5.824
2.362
0.591
5.566
0.027
5
0.474
62.588
5.613
0.202
7.936
5.024
0.402
4.558
4.047
0.653
8.958
0.017
6
0.586
54.954
8.288
0.281
8.920
7.497
0.408
3.227
4.796
0.554
11.051
0.025
7
0.690
49.758
10.678
0.351
9.222
9.874
0.537
2.346
4.594
0.443
12.124
0.074
8
0.781
46.278
12.648
0.387
9.087
12.118
0.709
1.838
3.968
0.365
12.434
0.168
9
0.856
43.875
14.178
0.407
8.669
14.181
0.897
1.559
3.340
0.314
12.253
0.328
10
0.918
42.063
15.330
0.422
8.143
15.939
1.092
1.411
2.922
0.283
11.822
0.571
NIKKEI
FTSE
BIBOR
Period
S.E.
2m
DJIA
NASDAQ
225
100
SSE
HSI
TSX
BOVESPA
ASX
DAX
1
0.112
100.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
2
0.195
93.816
0.637
0.473
0.011
0.912
1.728
1.908
0.020
0.255
0.030
0.211
3
0.269
84.648
0.816
0.298
1.858
3.169
1.261
5.367
0.726
0.153
1.585
0.120
4
0.362
71.931
3.340
0.171
4.944
4.815
0.733
6.100
2.583
0.229
5.083
0.071
5
0.471
60.932
6.651
0.126
6.977
7.298
0.487
4.668
4.304
0.192
8.322
0.042
6
0.582
53.201
9.668
0.157
7.780
9.922
0.514
3.231
5.065
0.128
10.272
0.061
7
0.684
47.863
12.331
0.187
7.967
12.325
0.668
2.340
4.826
0.098
11.249
0.146
8
0.772
44.218
14.530
0.196
7.758
14.535
0.862
1.863
4.154
0.088
11.504
0.291
9
0.846
41.650
16.233
0.197
7.289
16.552
1.071
1.631
3.495
0.083
11.286
0.515
10
0.907
39.673
17.511
0.199
6.735
18.251
1.285
1.533
3.068
0.077
10.829
0.838
NIKKEI
FTSE
BIBOR
Period
S.E.
3m
DJIA
NASDAQ
225
100
SSE
HSI
TSX
BOVESPA
ASX
DAX
1
0.113
100.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
2
0.202
94.254
0.604
0.201
0.000
0.821
1.530
1.414
0.074
0.408
0.042
0.652
3
0.279
85.917
0.736
0.123
1.836
3.236
1.135
4.212
0.948
0.215
1.262
0.382
4
0.372
73.622
3.269
0.091
4.778
5.128
0.670
4.848
2.883
0.195
4.289
0.228
5
0.481
62.834
6.661
0.055
6.570
7.728
0.450
3.642
4.674
0.150
7.093
0.144
6
0.590
55.231
9.748
0.048
7.148
10.432
0.477
2.478
5.487
0.099
8.739
0.113
7
0.690
49.880
12.490
0.048
7.198
12.868
0.624
1.825
5.259
0.083
9.547
0.179
8
0.777
46.122
14.783
0.044
6.930
15.067
0.810
1.541
4.562
0.079
9.740
0.322
9
0.848
43.402
16.573
0.039
6.450
17.056
1.010
1.465
3.865
0.077
9.514
0.548
10
0.908
41.258
17.925
0.038
5.914
18.730
1.216
1.496
3.396
0.071
9.078
0.877
NIKKEI
FTSE
Period
S.E.
6m
DJIA
NASDAQ
225
100
SSE
HSI
TSX
BOVESPA
ASX
DAX
1
0.113
100.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
2
0.210
93.955
0.529
0.076
0.001
0.960
0.926
1.069
0.130
0.963
0.003
1.389
3
0.291
86.368
0.574
0.061
1.370
3.678
0.782
3.167
1.118
0.667
1.274
0.941
BIBOR
837
International Journal of Economics and Financial Issues, Vol. 3, No. 4, 2013, pp.827-843
4
0.384
75.117
2.967
0.165
3.733
5.863
0.492
3.482
2.972
0.384
4.216
0.609
5
0.490
64.737
6.271
0.210
5.081
8.636
0.327
2.496
4.771
0.238
6.846
0.387
6
0.597
57.149
9.289
0.209
5.346
11.496
0.355
1.685
5.672
0.163
8.341
0.296
7
0.693
51.609
11.991
0.215
5.219
14.016
0.496
1.375
5.510
0.133
9.047
0.389
8
0.776
47.527
14.298
0.232
4.902
16.216
0.683
1.392
4.830
0.118
9.201
0.601
9
0.844
44.431
16.136
0.241
4.471
18.148
0.890
1.580
4.125
0.106
8.967
0.905
10
0.901
41.913
17.551
0.232
4.038
19.740
1.103
1.845
3.645
0.095
8.530
1.309
NIKKEI
FTSE
BIBOR
Period
S.E.
9m
DJIA
NASDAQ
225
100
SSE
HSI
TSX
BOVESPA
ASX
DAX
1
0.117
100.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
2
0.221
94.075
0.386
0.033
0.036
1.110
0.863
0.635
0.157
1.342
0.012
1.351
3
0.307
86.945
0.588
0.083
1.683
3.803
0.791
2.312
1.015
1.036
0.856
0.889
4
0.401
75.788
3.240
0.427
4.298
5.983
0.532
2.715
2.533
0.609
3.310
0.564
5
0.508
65.218
6.812
0.648
5.741
8.767
0.341
1.949
4.143
0.384
5.644
0.354
6
0.613
57.350
10.054
0.727
5.980
11.627
0.327
1.346
5.028
0.263
6.974
0.324
7
0.708
51.469
12.949
0.788
5.793
14.106
0.429
1.226
4.929
0.201
7.588
0.523
8
0.789
47.030
15.431
0.850
5.419
16.221
0.584
1.404
4.328
0.164
7.716
0.853
9
0.855
43.612
17.417
0.877
4.943
18.046
0.765
1.728
3.705
0.139
7.503
1.263
10
0.910
40.843
18.937
0.854
4.475
19.526
0.956
2.106
3.309
0.123
7.110
1.760
NIKKEI
FTSE
BIBOR
Period
S.E.
12m
DJIA
NASDAQ
225
100
SSE
HSI
TSX
BOVESPA
ASX
DAX
1
0.124
100.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
2
0.237
94.018
0.225
0.044
0.096
1.239
0.667
0.459
0.157
1.624
0.000
1.471
3
0.327
86.590
0.628
0.201
1.812
4.041
0.695
1.886
1.012
1.327
0.835
0.973
4
0.423
75.144
3.478
0.859
4.318
6.456
0.500
2.253
2.407
0.805
3.164
0.615
5
0.530
64.303
7.169
1.339
5.622
9.428
0.323
1.615
3.916
0.517
5.374
0.394
6
0.634
56.163
10.490
1.565
5.760
12.366
0.297
1.157
4.763
0.362
6.638
0.438
7
0.728
50.035
13.424
1.727
5.506
14.830
0.380
1.161
4.671
0.275
7.225
0.767
8
0.806
45.379
15.921
1.864
5.092
16.870
0.518
1.440
4.100
0.225
7.356
1.236
9
0.871
41.790
17.904
1.923
4.608
18.580
0.685
1.848
3.526
0.197
7.161
1.778
10
0.924
38.913
19.404
1.883
4.156
19.920
0.866
2.301
3.191
0.183
6.794
2.389
Cholesky Ordering: BIBOR, DJIA, NASDAQ, NIKKEI 225, FTSE 100, SSE, HIS, TSX, BOVESPA, ASX DAX
4.4 Impulse Response Function analysis
To clarify the relationship between BIBOR and stock index we will continue to test the impulse
responses in which this analysis able to make others know of relation between variables because of
the impulse responses analysis can show us a direction of relationship as negative or positive, more
than this we also know a short-run relationship and long-run relationship as well.
The impulse response’ graphs shown us the impact of stock indexes shocks on BIBOR as follows:
1.) The DJIA: the DJIA shock on BIBOR was positive relation in every tenor and increased from the
short-run throughout long-run however it was little fell in the long-run.
2.) The NASDAQ: the NASDAQ shock on BIBOR was positive relation in tenor 1 week, 1 month, 2
months and 3 months afterward in 6 months, 9 months, and 12 months was negative relation.
838
The Dynamic Relationship of Stock Indexes on Interbank Money Market Rates: Evidence from
Thailand
3.) The NIKKEI225: the NIKKEI225 shock on BIBOR was seems like the DJIA that it was positive
relation but the degree of change was less than DJIA.
4.) The FTSE100: the FTSE100 shock on BIBOR was positive relation in every tenor moreover it was
increased from short-run throughout long-run.
5.) The SSE: the SSE shock on BIBOR was seems like the NASDAQ but the degree of positive
relation on SSE shock is more than NASDAQ.
6.) The HSI: the HSI shock on BIBOR was positive relation in short-run but in long-run it was
negative relation especially from tenor 2 months till tenor 12 months.
7.) The TSX: the TSX shock on BIBOR was positive relation and it was increased in short-run
whereas in long-run it was decreased.
8.) The BOVESPA: the BOVESPA shock on BIBOR was negative relation especially in tenor 1 week
however after tenor 1 week the degree of negative relation was decreased.
9.) The ASX: the ASX shock on BIBOR was negative relation in every tenor of BIBOR and began
from short-run until long-run.
10.) The DAX: the DAX shock on BIBOR was negative relation in every tenor particularly in tenor
12 months which the degree of negative relation in short-run was less than long-run.
In sum up, from the impulse response analysis in which presented the response of the BIBOR to
changes in the stock indexes, this subsection found the stock indexes especially the DJIA, FTSE, and
ASX had more significant effect to BIBOR than other stock indexes due to the degree of relationship
on BIBOR of DJIA, FTSE, and ASX shocks were higher.
Figure 2. Impulse responses of BIBOR 1 week to stock index.
Response to Cholesky One S.D. Innovations ± 2 S.E.
Figure 3. Impulse responses of BIBOR 1 month to stock index.
839
International Journal of Economics and Financial Issues, Vol. 3, No. 4, 2013, pp.827-843
Figure 4. Impulse responses of BIBOR 2 months to stock index.
Figure 5. Impulse responses of BIBOR 3 months to stock index.
Figure 6. Impulse responses of BIBOR 6 months to stock index.
840
The Dynamic Relationship of Stock Indexes on Interbank Money Market Rates: Evidence from
Thailand
Figure 7. Impulse responses of BIBOR 9 months to stock index.
Figure 8. Impulse responses of BIBOR 12 months to stock index.
5. Conclusions
There are several reason to suspect that the correlation between interbank money market rates and
stock indexes. In this study we attempt to investigate the dynamic relationship between the interbank
money market and the stock market by description the correlation of the BIBOR and stock indexes
during the sample period that begin from January 2006 to December 2011. Therefore, to explain how
the performance of the stock indexes affect BIBOR changes and how much the degree of movement
in stock indexes impact on BIBOR we have used the econometric test, such as the granger causality
test, variance decomposition test, and impulse response test, to be our tools then analyzed that
relationship between variables.
Accordingly, in recent research we have found the empirical result from the econometric test such
the unit root test with ADF has reported the BIBOR in every tenor and the stock indexes was
stationary at 1st difference in which following the rule and regulation of stationary that is the t-statistic
value must less than the MacKinnon critical value. From the Granger causality analysis, this test
found the changes in the DJIA, NASDAQ, NIKKEI225, FTSE100, TSX, SSE, BOVESPA, ASX, and
DAX were causality on BIBOR, except the HSI. Next, to explain the dynamic relationship of stock
indexes on BIBOR we have used the variance decomposition analysis and the impulse response
analysis. The result from variance decomposition analysis found the DJIA, FTSE100, and ASX had
more significant relative to BIBOR than other stock indexes. The impulse response analysis found the
degree of relationship of DJIA, FTSE100, and ASX were higher started from the short-run throughout
long-run. Likewise, all of the stock indexes were vary directly with the tenor of BIBOR. All in all,
841
International Journal of Economics and Financial Issues, Vol. 3, No. 4, 2013, pp.827-843
there are three stock indexes which seem to have a major interrelation and important determinants in
the BIBOR changes better than other stock indexes that consist of DJIA, FTSE100, and ASX over the
sample period.
For the future study, should be studied in another stock index and use another variable that may
influence the BIBOR, for explore the dynamic relationship on it.
Acknowledgements
We would like to thank you Assoc. Prof. Poonsag Sangsunt who provide any suggestions during
the research and service team of Bank of Thailand (BOT) for their supporting data.
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